Embodiments of the present invention provide a device for determining objects in a color recording, as it may, for example, be used for determining plasmodia, for example in thick blood films. Further embodiments provide a method for determining objects in a color recording, as it may, for example, be used for determining plasmodia, for example in a thick blood film.
Malaria is a tropical disease caused by the infection of red blood cells (erythrocytes) with single-cell parasites of the species of Plasmodium. The disease is transmitted by female mosquitoes of the anopheles species from human to human. Four classes of plasmodia are relevant for human beings: Plasmodium falciparum, Plasmodium vivax, Plasmodium ovale and Plasmodium malariae. The most severe forms of malaria are caused by Plasmodium falciparum. The number of annual new infections is estimated by the World Health Organization (WHO) to be 300 to 500 million cases. Every year approximately 1 million people die of malaria. In 2008 approximately 243 million cases led to approximately 863,000 deaths.
According to the World Health Organization (WHO), microscopic examinations of colored blood films are the “gold standard” for the diagnosis of malaria. Two types of blood films are used for diagnosing malaria: thin and thick ones. A thick blood film is used for the search for malaria parasites, as the same consists of many layers of blood cells and enables the examination of relatively large amounts of blood. Often, however, the types of parasite (e.g. Plasmodium vivax) may not be confirmed merely on the basis of a thick film. Thus, for characterizing the types of parasite detected in the thick blood film, conventionally a thin film is produced. According to the WHO, a routine examination of thick films is based on the examination of at least 100 microscopic visual fields with great magnification (objective×100, ocular×10). This method is long and tiring for a laboratory assistant and necessitates special training and extensive specialist knowledge. Apart from that, it seems to be error-prone, as studies have shown that in the resulting quantifications of a parasite density there is strong variability within and among observers. Thus, CADe systems (CADe=computer-aided detection) are necessitated which help laboratory assistants when detecting and counting plasmodia in thick blood films. In FIG. 13, exemplary plasmodia are illustrated cut out of images of thick blood films.
Despite great progress in the field of malaria diagnosis, light microscopy is still the most important technique for the diagnosis of this disease, see also [Guidelines for the Treatment of Malaria. Second Edition. World Health Organization 2010]. With this technology, using a light microscope, on the basis of thick and thin blood films plasmodia are detected and characterized in the blood.
In this respect, FIG. 8 shows a recording of a thin blood film 801 (at the top in FIG. 8) and a thick blood film 802 (at the bottom in FIG. 8). With the thin film a small amount of blood is spread across a large area. With the thick film a larger amount of blood is spread on a round area with a diameter of approximately 1 cm. The concentration of the blood with the thick film is approximately 20× higher than with the thin film, as may clearly be seen in FIG. 8 due to the stronger coloring of the thick blood film 802.
In early stages of the malaria disease the plasmodia in the blood may be present at extremely low concentrations. The microscopic search for plasmodia in blood films often corresponds to the proverbial needle in a haystack. For this reason the microscopic diagnosis of malaria is very time-consuming and causes comparatively high costs. Due to the low concentrations of plasmodia in the blood, the detection of plasmodia is performed according to the guidelines of the WHO on the basis of a thick blood film (as is exemplarily shown in FIG. 8 as a thick blood film 802). With a thick blood film, the erythrocytes are hemolyzed (dissolved) by water in the coloring solution. By enrichment or concentration, per visual field approximately 20 to 40 times more plasmodia exist in a thick blood film than in a thin blood film (as is exemplarily illustrated in FIG. 8 as a thin blood film 802).
Prototypes for computer-automated microscopy methods/systems exist (computer-automated microscopy CAM) which automate the search for plasmodia on the basis of a thin blood film. These systems/methods detect and segment first of all erythrocytes in digital recordings of the thin blood film and characterize the detected erythrocytes as “infected by plasmodia” or as “not infected by plasmodia”. In the following, some documents are briefly to be mentioned which deal with the detection of plasmodia in blood films.
A rougher overview of currently used methods is disclosed in a conference article by Tek et al. [see also: F. B. Tek, A. G. Dempster, and I. Kale. Computer vision for microscopy diagnosis of malaria. Malaria Journal, 8(1): 153, 2009].
The detection of plasmodia on the basis of thick films is the recommended standard proposed by the WHO and is approximately ten times more sensitive than on the basis of thin films [see also: World Health Organization. World malaria report 2009. WHO Press, 2009].
Still, only one single article published by Frean [see also: J. Frean. Reliable enumeration of malaria parasites in thick blood films using digital image analysis. Malaria Journal, 8(1): 218, 2009] covers the automatic detection of plasmodia in thick films. Frean proposes a direct approach of detection on the basis of freely accessible software which is capable of estimating average to large parasite densities with high accuracy but is not suitable for smaller plasmodia densities (less than six parasites per image).
Diaz et al. [see also: G. Diaz, F. A. Gonzalez, and E. Romero. A semi-automatic method for quantification and classification of erythrocytes infected with malaria parasites in microscopic images. J. of Biomedical Informatics, 42(2): 296-307, 2009] proposed an approach for the quantification and classification of erythrocytes infected with Plasmodium falciparum. Their approach comprises a segmentation and a classification stage. In the segmentation stage erythrocytes are identified and segmented using a luminance correction, pixel classification and an inclusion tree illustration. In the classification stage infected erythrocytes are identified and different infection stages are characterized.
The documents WO 03/102210 A2 and US 2004/0241677 A1 describe a method for an automatic analysis of a biological sample using a light microscope. As a possibility of application the search for objects in cells is mentioned. As an example, in particular the search for plasmodia in erythrocytes is mentioned. This is an analysis technique for thin blood films, as no erythrocytes are preserved in thick blood films.
The document WO 2005/121863 A1 describes a method for the automatic diagnosis of malaria and other infection diseases (e.g.: tuberculosis, Chlamydia). The document describes a CAM system whose central idea represents image analysis methods on the basis of the so-called morphological granulometry. The general procedure for the detection of plasmodia is based on the characterization of erythrocytes as “infected by plasmodia” or “not infected by plasmodia”. This is an analysis method for thin blood films.
The document [ROSS, N. E. C. J. PRITCHARD, D. L. RUBIN and A. G. DUSE: Automated image processing method for the diagnosis and classification of malaria on thin blood smears. Medical and Biological Engineering and Computing, 44(5):427-436, 2006] describes the CAM system disclosed in the above-mentioned document WO 2005/121863 A1.
The journal article [LE, M.-T., T. R. BRETSCHNEIDER, S. JUSS and P. R. PREISER: A novel semi-automatic imagine processing approach to determine Plasmodium falciparum parasitemia in Giemsa-stained thin blood smears. BMC Cell Biol., 9:15, 2008] describes a current study regarding the automatic detection of plasmodia in thin blood films. The described methods necessitate the possibility of locating erythrocytes and for this reason are, among others, not transferable to thick blood films. Their approach is based on an analysis of the common localization of detected erythrocytes and potential plasmodia. Although their work is based on thin films, which is particularly suitable for differentiating species, their work does not cover this aspect.
In the journal article [EDEN, M., J. E. GREEN and A. SUN: Feasibility of computer screening of blood films for the detection of malaria parasites. Bull World Health Organ, 48(2):211-218, 1973] a more simple method for characterizing erythrocytes with the aim of detecting plasmodia is described. Also this method is not transferable to thick blood films.
In the documents [DEMPASTER, A. G: and C. DI RUBERTO: Using granulometries in processing images of malarial blood. In: Proc. IEEE International Symposium on Circuits and Systems ISCAS 2001, Vol. 5, pp. 291-294, 6-9 May 2001]; [DI RUBERTO, C., A. DEMPSTER, S. KHAN and B. JARRA: Automatic thresholding of infected blood images using granulometry and regional extrema. In: Proc. 15th International Conference on Pattern Recognition, Vol. 3, pp. 441-444, 3-7 Sep. 2000]; [DI RUBERTO, C., A. DEMPSTER, S. KHAN and B. JARRA: Segmentation of blood images using morphological operators. In: Proc. 15th International Conference on Pattern Recognition, Vol. 3, pp. 397-400, 3-7 Sep. 2000]; [DI RUBERTO, C., A. DEMPSTER, S. KHAN and B. JARRA: Morphological Image Processing for Evaluating Malaria Disease. In: IWVF-4: Proceedings of the 4th International Workshop on Visual Form, pp. 739-748, London. UK, 2001. Springer Verlag]; and [DI RUBERTO, C., A. DEMPSTER, S. KHAN and B. JARRA: Analysis of infected blood cell images using morphological operators. Image and Vision Computing, 20(2):133-146, February 2002] a CAM method for the analysis of thin blood films and for the detection of plasmodia is described. Also this system or method is based on the analysis of erythrocytes and thus on thin blood films.
The document [HALIM, S., T. R. BRETSCHNEIDER, Y. LI, P. R. PREISER and C. KUSS: Estimating Malaria Parasitaemia from Blood Smear Images. IN: Proc. 9th International Conference on Control, Automation, Robotics and Vision ICARCV, 06, pp. 1-6, 5-8 Dec. 2006] describes a method for the analysis of erythrocytes with the aim of plasmodia detection.
The document [TEK, F. B., A. G. DEMPSTER and I. KALE: Malaria parasite detection in peripheral blood images. In British Machine Vision Conference 2006 (BMVC2006), pp. 347-356, 2006] describes a method for the analysis of thin blood films with the aim of plasmodia detection. In contrast to the above-mentioned methods, here a classification pixel-by-pixel for a direct detection of plasmodia is used. A Bayesian pixel classification is used to find plasmodia candidates in the first stage. A kNN classifier which is based on features regarding form, histogram and statistical moments is used in a second stage in order to reduce the number of false positive detections.
The above-mentioned methods have in common that they do not enable a reliable detection of plasmodia even at early stages of the mentioned malaria infection.